See also [articles about VictoriaMetrics from our users](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/Articles#third-party-articles-and-slides).
[zhihu](https://www.zhihu.com) is the largest chinese question-and-answer website. We use VictoriaMetrics to store and use Graphite metrics, and we shared the [promate](https://github.com/zhihu/promate) solution in our [单机 20 亿指标，知乎 Graphite 极致优化！](https://qcon.infoq.cn/2020/shenzhen/presentation/2881)([slides](https://static001.geekbang.org/con/76/pdf/828698018/file/%E5%8D%95%E6%9C%BA%2020%20%E4%BA%BF%E6%8C%87%E6%A0%87%EF%BC%8C%E7%9F%A5%E4%B9%8E%20Graphite%20%E6%9E%81%E8%87%B4%E4%BC%98%E5%8C%96%EF%BC%81-%E7%86%8A%E8%B1%B9.pdf)) talk at [QCon 2020](https://qcon.infoq.cn/2020/shenzhen/).
Numbers:
- Active time series: ~2500 Million
- Datapoints: ~20 Trillion
- Ingestion rate: ~1800k/s
- Disk usage: ~20 TiB
- Index size: ~600 GiB
- The average query rate is ~3k per second (mostly alert queries).
- Query duration: median is ~40ms, 99th percentile is ~100ms.
See [slides](https://promcon.io/2019-munich/slides/remote-write-storage-wars.pdf) and [video](https://youtu.be/OsH6gPdxR4s)
from [Remote Write Storage Wars](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) talk at [PromCon 2019](https://promcon.io/2019-munich/).
VictoriaMetrics is compared to Thanos, Corex and M3DB in the talk.
The European Organization for Nuclear Research known as [CERN](https://home.cern/) uses VictoriaMetrics for real-time monitoring
of the [CMS](https://home.cern/science/experiments/cms) detector system.
According to [published talk](https://indico.cern.ch/event/877333/contributions/3696707/attachments/1972189/3281133/CMS_mon_RD_for_opInt.pdf)
VictoriaMetrics is used for the following purposes as a part of "CMS Monitoring cluster":
* As long-term storage for messages consumed from the [NATS messaging system](https://nats.io/). Consumed messages are pushed directly to VictoriaMetrics via HTTP protocol
* As long-term storage for Prometheus monitoring system (30 days retention policy, there are plans to increase it up to ½ year)
* As a data source for visualizing metrics in Grafana.
R&D topic: Evaluate VictoraMetrics vs InfluxDB for large cardinality data.
See also [The CMS monitoring infrastructure and applications](https://arxiv.org/pdf/2007.03630.pdf) publication from CERN with details about VictoriaMetrics usage.
[COLOPL](http://www.colopl.co.jp/en/) is Japaneese Game Development company. It started using VictoriaMetrics
after evaulating the following remote storage solutions for Prometheus:
* Cortex
* Thanos
* M3DB
* VictoriaMetrics
See [slides](https://speakerdeck.com/inletorder/monitoring-platform-with-victoria-metrics) and [video](https://www.youtube.com/watch?v=hUpHIluxw80)
from `Large-scale, super-load system monitoring platform built with VictoriaMetrics` talk at [Prometheus Meetup Tokyo #3](https://prometheus.connpass.com/event/157721/).
[Zerodha](https://zerodha.com/) is India's largest stock broker. Monitoring team at Zerodha faced with the following requirements:
* Multiple K8s clusters to monitor
* Consistent monitoring infra for each cluster across the fleet
* Ability to handle billions of timeseries events at any point of time
* Easier to operate and cost effective
Thanos, Cortex and VictoriaMetrics were evaluated as a long-term storage for Prometheus. VictoriaMetrics has been selected due to the following reasons:
* Blazing fast benchmarks for a single node setup.
* Single binary mode. Easy to scale vertically, very less operational headache.
* [MetricsQL](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/MetricsQL) gives us the ability to extend PromQL with more aggregation operators.
* API is compatible with Prometheus, almost all standard PromQL queries just work out of the box.
* Handles storage well, with periodic compaction. Makes it easy to take snapshots.
See [Monitoring K8S with VictoriaMetrics](https://docs.google.com/presentation/d/1g7yUyVEaAp4tPuRy-MZbPXKqJ1z78_5VKuV841aQfsg/edit) slides,
[video](https://youtu.be/ZJQYW-cFOms) and [Infrastructure monitoring with Prometheus at Zerodha](https://zerodha.tech/blog/infra-monitoring-at-zerodha/) blog post for more details.
[Wix.com](https://en.wikipedia.org/wiki/Wix.com) is the leading web development platform.
> We needed to redesign metric infrastructure from the ground up after the move to Kubernethes. A few approaches/designs have been tried before the one that works great has been chosen: Prometheus instance in every datacenter with 2 hours retention for local storage and remote write into [HA pair of single-node VictoriaMetrics instances](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/README.md#high-availability).
> Optimizing for those points and our specific workload VictoriaMetrics proved to be the best option. As an icing on a cake we’ve got [PromQL extensions](https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/MetricsQL) - `default 0` and `histogram` are my favorite ones, for example. What we specially like is having a lot of tsdb params easily available via config options, that makes tsdb easy to tune for specific use case. Also worth noting is a great community in [Slack channel](http://slack.victoriametrics.com/) and of course maintainer support.
> [Wedos](https://www.wedos.com/) is the Biggest Czech Hosting. We have our own private data center, that holds only our servers and technologies. The second data center, where the servers will be cooled in an oil bath, is being built. We started using [cluster VictoriaMetrics](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/cluster/README.md) to store Prometheus metrics from all our infrastructure after receiving positive references from our friends who successfully use VictoriaMetrics.
Numbers:
* The number of acitve time series: 5M.
* Ingestion rate: 170K data points per second.
* Query duration: median is ~2ms, 99th percentile is ~50ms.
> We like configuration simplicity and zero maintenance for VictoriaMetrics - once installed and forgot about it. It works out of the box without any issues.
The mission of [MHI Vestas Offshore Wind](http://www.mhivestasoffshore.com) is to co-develop offshore wind as an economically viable and sustainable energy resource to benefit future generations.
MHI Vestas Offshore Wind is using VictoriaMetrics to ingest and visualize sensor data from offshore wind turbines. The very efficient storage and ability to backfill was key in chosing VictoriaMetrics. MHI Vestas Offshore Wind is running the cluster version of VictoriaMetrics on Kubernetes using the Helm charts for deployment to be able to scale up capacity as the solution will be rolled out.
[Brandwatch](https://www.brandwatch.com/) is the world's pioneering digital consumer intelligence suite,
helping over 2,000 of the world's most admired brands and agencies to make insightful, data-driven business decisions.
The engineering department at Brandwatch has been using InfluxDB for storing application metrics for many years
and when End-of-Life of InfluxDB version 1.x was announced we decided to re-evaluate our whole metrics collection and storage stack.
Main goals for the new metrics stack were:
- improved performance
- lower maintenance
- support for native clustering in open source version
- the less metrics shipment had to change, the better
- achieving longer data retention would be great but not critical
We initially looked at CrateDB and TimescaleDB which both turned out to have limitations or requirements in the open source versions
that made them unfit for our use case. Prometheus was also considered but push vs. pull metrics was a big change we did not want
to include in the already significant change.
Once we found VictoriaMetrics it solved the following problems:
- it is very light weight and we can now run virtual machines instead of dedicated hardware machines for metrics storage
- very short startup time and any possible gaps in data can easily be filled in by using Promxy
- we could continue using Telegraf as our metrics agent and ship identical metrics to both InfluxDB and VictoriaMetrics during a migration period (migration just about to start)
- compression is really good so we can store more metrics and we can spin up new VictoriaMetrics instances
for new data and keep read-only nodes with older data if we need to extend our retention period further
than single virtual machine disks allow and we can aggregate all the data from VictoriaMetrics with Promxy
High availability is done the same way we did with InfluxDB, by running parallel single nodes of VictoriaMetrics.
Numbers:
- active time series: up to 25 million
- ingestion rate: ~300 000
- total number of datapoints: 380 billion and growing
- total number of entries in inverted index: 575 million and growing
- daily time series churn rate: ~550 000
- data size on disk: ~660GB and growing
- index size on disk: ~9,3GB and growing
- average datapoint size on disk: ~1.75 bytes
Query rates are insignificant as we have concentrated on data ingestion so far.
Anders Bomberg, Monitoring and Infrastructure Team Lead, brandwatch.com
- VictoriaMetrics didn't support replication (it [supports replication now](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/cluster/README.md#replication-and-data-safety)) - we run extra instance of VictoriaMetrics and Promxy in front of VictoriaMetrics pair for high availability.
- VictoriaMetrics stores 1 extra month for defined retention (if retention is set to N months, then VM stores N+1 months of data), but this is still better than other solutions.
Some numbers from our single-node VictoriaMetrics setup:
- active time series: 10M
- ingestion rate: 800K samples/sec
- total number of datapoints: more than 2 trillion
- total number of entries in inverted index: more than 1 billion
[The Academic and Research Network of Slovenia](https://www.arnes.si/en/) (ARNES) is a public institute that provides network services to research,
educational and cultural organizations, and enables them to establish connections and cooperation with each other and with related organizations abroad.
After using Cacti, Graphite and StatsD for years, we wanted to upgrade our monitoring stack to something that:
- has native alerting support
- can run on-prem
- has multi-dimension metrics
- lower hardware requirements
- is scalable
- simple client provisioning and discovery with Puppet
We were running Prometheus for about a year in a test environment and it worked great. But there was a need/wish for a few years of retention time,
like the old systems provided. We tested Thanos, which was a bit resource hungry back then, but it worked great for about half a year
until we discovered VictoriaMetrics. As our scale is not that big, we don't have on-prem S3 and no Kubernetes, VM's single node instance provided
the same result with less maintenance overhead and lower hardware requirements.
After testing it a few months and having great support from the maintainers on [Slack](http://slack.victoriametrics.com/),
we decided to go with it. VM's support for ingesting InfluxDB metrics was an additional bonus, since our hardware team uses
SNMPCollector to collect metrics from network devices and switching from InfluxDB to VictoriaMetrics was a simple change in the config file for them.
We are running 1 Prometheus, 1 VictoriaMetrics and 1 Grafana server in each datacenter on baremetal servers, scraping 350+ targets
(and 3k+ devices collected via SNMPCollector sending metrics directly to VM). Each Prometheus is scraping all targets,
so we have all metrics in both VictoriaMetrics instances. We are using [Promxy](https://github.com/jacksontj/promxy) to deduplicate metrics from both instances.
Grafana has a LB infront, so if one DC has problems, we can still view all metrics from both DCs on the other Grafana instance.
[idealo.de](https://www.idealo.de/) is the leading price comparison website in Germany. We use Prometheus for metrics on our container platform.
When we introduced Prometheus at idealo we started with m3db as a longterm storage. In our setup m3db was quite unstable and consumed a lot of resources.
VictoriaMetrics runs very stable for us and uses only a fraction of the resources. Although we also increased our retention time from 1 month to 13 months.
Numbers:
- The number of active time series per VictoriaMetrics instance is 21M.
- Total ingestion rate 120k metrics per second.
- The total number of datapoints 3.1 trillion.
- The average time series churn rate is ~9M per day.
- The average query rate is ~20 per second. Response time for 99th quantile is 120ms.
